”As the student accommodates this new learning into their LTM, then the complexity of the next task can be increased. As Ericsson and Kintsch (1995) found, the limitations of the WM effectively disappear when the LTM can be used to support intrinsic load.”

Most teachers and tutors have experienced the scenario where a student who has returned from a long absence is unable to cope with the current learning. Unfortunately, it inevitably leads to further disengagement. The reason is that normally learning is a sequential affair, and each new step needs to be understood before the next one is introduced.

But this intuitive understanding can also be explained scientifically: the student can’t sufficiently connect the new learning in front of them to information they have in their long-term memory (LTM).

It’s all about intrinsic load

Any new learning has an element of complexity associated with it. In cognitive load theory (Sweller et al 2019), this complexity is referred to as intrinsic load. In learning, intrinsic load is desirable, otherwise there’s no challenge, and usually no value in the activity, but we also have to ensure that the intrinsic load does not overwhelm the area of the brain that tries to encode it. This area is the working memory (WM). An overloaded WM results in cognitive overload, and reduced learning.

WM and LTM working together

When the WM attempts to encode the intrinsic load, it searches the LTM to see if there is a schema or bank of knowledge on the topic it recognises. It does this not only as a logical function of deciding if new knowledge can be added to it, but also because it has a limited capacity (Cowan, 2005).

The limited capacity is an evolutionary stroke of genius. If it wasn’t limited, we would be constantly overwhelmed with processing EVERY STIMULUS around us every second of the day. But also a stroke of genius is the ability of the WM to use the LTM as a mitigating force.

If elements of the intrinsic load of a task being encoded in the WM can be recognised in an existing schema, then those elements do not weigh on the WM capacity. “The limitations of the WM effectively disappear when the LTM can be used to support intrinsic load.”

Let’s imagine a task that inherently has five or six components that make up its complexity:

The problem, ‘Find the value of x if 3x + 3 = 12’ involves more than five components of knowledge to solve: algebraic notation, balancing equations, order of operations, multiplication, division, addition and subtraction. It is clear that if you gave such a problem to someone with absolutely no mathematical knowledge the problem would be impossible for them to solve. However, for the student whose WM is able to successfully search the LTM and make connections to existing schemas of addition, subtraction, multiplication and division and even order of operations, the task’s intrinsic load is now essentially only related to algebraic notation and to the balancing of equations. The other components don’t take up valuable WM real-estate – they are automatically recalled to assist in processing the unknown components. As a result, the task is made significantly easier.

The picture tells a 1000 words

In the wonderful video below, created by Aaron Honson and Georgia Forrest, there are two contexts illustrated.

  • Both are influenced similarly by the environmental extraneous load, indicated by the red liquid, naturally occupying a certain percentage of the available working memory capacity. We assume that the design of the learning environment has done all it can to reduce this amount, and so it is standard in both contexts.
  • As a task is introduced (blue liquid) that contains intrinsic load, the working memory is again impacted on, but still comfortably within its limits.
  • But it is when the complexity or intrinsic load of the task increases in the video’s third movement that the two contexts then differ. On the left, the schema is not activated – the student cannot make any connections to the new learning context and so the working memory is taken to capacity – no new information can be processed. On the right side however, the schema is firing – the student can connect the intrinsic load to previous learning, and thus the working memory is not as heavily impacted. For this student, the implication is that the intrinsic load can be increased.

Implications for learning design

  1. The intrinsic load of learning can be increased if students can connect aspects of the learning to a related schema in their LTMs
  2. The sooner schemas are established, the faster intrinsic load can be increased
  3. There are deliberate actions and learning strategies that encourage schema development and assist in retention
  4. Encouraging students to make connections between ideas and existing knowledge, scaffolds the breakdown of high levels of complexity in tasks.


Cowan, N. (2005). Working memory capacity. New York: Psychology Press.

Ericsson, K. , Kintsch, W. & (1995). Long-Term Working Memory. Psychological Review, 102 (2), 211-245.

Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive Architecture and Instructional Design: 20 Years Later. Educational Psychology Review31(2), 261–292

I’m Paul Moss. I’m a learning designer at the University of Adelaide. Follow me on Twitter @edmerger

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